Inhibition of Mycobacterium tuberculosis RNA Polymerase by Binding of a Gre Factor Homo log to the Secondary Channel

Because of its essential nature, each step of transcription, viz., initiation, elongation, and termination, is subjected to elaborate regulation. A number of transcription factors modulate the rates of transcription at these different steps, and several inhibitors shut down the process. Many modulators, including small molecules and proteinaceous inhibitors, bind the RNA polymerase (RNAP) secondary channel to control transcription. We describe here the first small protein inhibitor of transcription in Mycobacterium tuberculosis. Rv3788 is a homolog of the Gre factors that binds near the secondary channel of RNAP to inhibit transcription. The factor also affected the action of guanosine pentaphosphate (pppGpp) on transcription and abrogated Gre action, indicating its function in the modulation of the catalytic center of RNAP. Although it has a Gre factor-like domain organization with the conserved acidic residues in the N terminus and retains interaction with RNAP, the factor did not show any transcript cleavage stimulatory activity. Unlike Rv3788, another Gre homolog from Mycobacterium smegmatis, MSMEG_6292 did not exhibit transcription-inhibitory activities, hinting at the importance of the former in influencing the lifestyle of M. tuberculosis.

Moderate intensity static magnetic field has bactericidal effect on E. coli and S. epidermidis on sintered hydroxyapatite

The application of electromagnetic field in the context of bacteria associated infections on biomaterial surfaces has not been extensively explored. In this work, we applied a moderate intensity static magnetic field (100 mT) to understand the adhesion and growth behavior of both gram positive (S. epidermidis) and gram negative bacteria (E. coli) and also to investigate bactericidal/bacteriostatic property of the applied electromagnetic field. An in-house built magnetometer was used to apply static homogeneous magnetic field during a planned set of in vitro experiments. Both the sintered hydroxyapatite (HA) and the control samples seeded with bacteria were exposed to the magnetic field (100 mT) for different timescale during their log phase growth. Quantitative analysis of the SEM images confirms the effect of electromagnetic field on suppressing bacterial growth. Furthermore, cell integrity and inner membrane permeabilization assays were performed to understand the origin of such effect. The results of these assays were statistically analyzed to reveal the bactericidal effect of magnetic field, indicating cell membrane damage. Under the investigated culture conditions, the bactericidal effect was found to be less effective for S. Epidermidis than E. coli. (c) 2012 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 2012:100B:12061217, 2012.

An experimental study on cracking evolution in concrete and cement mortar by the b-value analysis of acoustic emission technique

Notched three point bend (TPB) specimens made with plain concrete and cement mortar were tested under crack mouth opening displacement (CMOD) control at a rate of 0.0004 mm/s and simultaneously acoustic emissions (AE) released were recorded during the experiments. Amplitude distribution analysis of AE released during concrete was carried out to study the development of fracture process in concrete and mortar specimens. The slope of the log-linear frequency-amplitude distribution of AE is known as the AE based b-value. The AE based b-value was computed in terms of physical process of time varying applied load using cumulative frequency distribution (Gutenberg-Richter relationship) and discrete frequency distribution (Aki's method) of AE released during concrete fracture. AE characteristics of plain concrete and cement mortar were studied and discussed and it was observed that the AE based b-value analysis serves as a tool to identify the damage in concrete structural members. (C) 2012 Elsevier Ltd. All rights reserved.

Impact of pit-toilet leachate on groundwater chemistry and role of vadose zone in removal of nitrate and E-coli pollutants in Kolar District, Karnataka, India

Assessment of chemistry of groundwater infiltrated by pit-toilet leachate and contaminant removal by vadose zone form the focus of this study. The study area is Mulbagal Town in Karnataka State, India. Groundwater level measurements and estimation of unsaturated permeability indicated that the leachate recharged the groundwater inside the town at the rate of 1 m/day. The average nitrate concentration of groundwater inside the town (148 mg/L) was three times larger than the permissible limit (45 mg/L), while the average nitrate concentration of groundwater outside the town (30 mg/L) was below the permissible limit. The groundwater inside the town exhibited E. coli contamination, while groundwater outside the town was free of pathogen contamination. Infiltration of alkalis (Na+, K+) and strong acids (Cl-, SO4 (2-)) caused the mixed Ca-Mg-Cl type (60 %) and Na-Cl type (28 %) facies to predominate groundwater inside the town, while, Ca-HCO3 (35 %), mixed Ca-Mg-Cl type (35 %) and mixed Ca-Na-HCO3 type (28 %) facies predominated groundwater outside/periphery of town. Reductions in E. coli and nitrate concentrations with vadose zone thickness indicated its participation in contaminant removal. A 4-m thickness of unsaturated sand + soft, disintegrated weathered rock deposit facilitates the removal of 1 log of E. coli pathogen. The anoxic conditions prevailing in the deeper layers of the vadose zone (> 19 m thickness) favor denitrification resulting in lower nitrate concentrations (28-96 mg/L) in deeper water tables (located at depths of -29 to -39 m).

On finding a set of healthy individuals from a large population

In this paper, we explore fundamental limits on the number of tests required to identify a given number of ``healthy'' items from a large population containing a small number of ``defective'' items, in a nonadaptive group testing framework. Specifically, we derive mutual information-based upper bounds on the number of tests required to identify the required number of healthy items. Our results show that an impressive reduction in the number of tests is achievable compared to the conventional approach of using classical group testing to first identify the defective items and then pick the required number of healthy items from the complement set. For example, to identify L healthy items out of a population of N items containing K defective items, when the tests are reliable, our results show that O(K(L - 1)/(N - K)) measurements are sufficient. In contrast, the conventional approach requires O(K log(N/K)) measurements. We derive our results in a general sparse signal setup, and hence, they are applicable to other sparse signal-based applications such as compressive sensing also.

Influence of Spatial Variability on Pavement Responses Using Latin Hypercube Sampling on Two-Dimensional Random Fields

Although uncertainties in material properties have been addressed in the design of flexible pavements, most current modeling techniques assume that pavement layers are homogeneous. The paper addresses the influence of the spatial variability of the resilient moduli of pavement layers by evaluating the effect of the variance and correlation length on the pavement responses to loading. The integration of the spatially varying log-normal random field with the finite-difference method has been achieved through an exponential autocorrelation function. The variation in the correlation length was found to have a marginal effect on the mean values of the critical strains and a noticeable effect on the standard deviation which decreases with decreases in correlation length. This reduction in the variance arises because of the spatial averaging phenomenon over the softer and stiffer zones generated because of spatial variability. The increase in the mean value of critical strains with decreasing correlation length, although minor, illustrates that pavement performance is adversely affected by the presence of spatially varying layers. The study also confirmed that the higher the variability in the pavement layer moduli, introduced through a higher value of coefficient of variation (COV), the higher the variability in the pavement response. The study concludes that ignoring spatial variability by modeling the pavement layers as homogeneous that have very short correlation lengths can result in the underestimation of the critical strains and thus an inaccurate assessment of the pavement performance. (C) 2014 American Society of Civil Engineers.

On Whitespace Identification Using Randomly Deployed Sensors

This work considers the identification of the available whitespace, i.e., the regions that do not contain any existing transmitter within a given geographical area. To this end, n sensors are deployed at random locations within the area. These sensors detect for the presence of a transmitter within their radio range r(s) using a binary sensing model, and their individual decisions are combined to estimate the available whitespace. The limiting behavior of the recovered whitespace as a function of n and r(s) is analyzed. It is shown that both the fraction of the available whitespace that the nodes fail to recover as well as their radio range optimally scale as log(n)/n as n gets large. The problem of minimizing the sum absolute error in transmitter localization is also analyzed, and the corresponding optimal scaling of the radio range and the necessary minimum transmitter separation is determined.

Maximum likelihood analysis of multi-stress accelerated life test data of series systems with competing log-normal causes of failure

This article presents frequentist inference of accelerated life test data of series systems with independent log-normal component lifetimes. The means of the component log-lifetimes are assumed to depend on the stress variables through a linear stress translation function that can accommodate the standard stress translation functions in the literature. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters. The maximum likelihood estimates are then further refined by bootstrap, which is also used to infer about the component and system reliability metrics at usage stresses. The developed methodology is illustrated by analyzing a real as well as a simulated dataset. A simulation study is also carried out to judge the effectiveness of the bootstrap. It is found that in this model, application of bootstrap results in significant improvement over the simple maximum likelihood estimates.

Robust Whisper Activity Detection Using Long-Term Log Energy Variation of Sub-Band Signal

The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to-noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD.

Cytotoxicity of Ultrasmall Gold Nanoparticles on Planktonic and Biofilm Encapsulated Gram-Positive Staphylococci

The emergence of multidrug resistant bacteria, especially biofilm-associated Staphylococci, urgently requires novel antimicrobial agents. The antibacterial activity of ultrasmall gold nanoparticles (AuNPs) is tested against two gram positive: S. aureus and S. epidermidis and two gram negative: Escherichia coli and Pseudomonas aeruginosa strains. Ultrasmall AuNPs with core diameters of 0.8 and 1.4 nm and a triphenylphosphine-monosulfonate shell (Au0.8MS and Au1.4MS) both have minimum inhibitory concentration (MIC) and minimum bactericidal concentration of 25 x 10(-6)m Au]. Disc agar diffusion test demonstrates greater bactericidal activity of the Au0.8MS nanoparticles over Au1.4MS. In contrast, thiol-stabilized AuNPs with a diameter of 1.9 nm (AuroVist) cause no significant toxicity in any of the bacterial strains. Ultrasmall AuNPs cause a near 5 log bacterial growth reduction in the first 5 h of exposure, and incomplete recovery after 21 h. Bacteria show marked membrane blebbing and lysis in biofilm-associated bacteria treated with ultrasmall AuNP. Importantly, a twofold MIC dosage of Au0.8MS and Au1.4MS each cause around 80%-90% reduction in the viability of Staphylococci enveloped in biofilms. Altogether, this study demonstrates potential therapeutic activity of ultrasmall AuNPs as an effective treatment option against staphylococcal infections.

Inner Bound on the GDOF of the K-User MIMO Gaussian Symmetric Interference Channel

The K-user multiple input multiple output (MIMO) Gaussian symmetric interference channel where each transmitter has M antennas and each receiver has N antennas is studied from a generalized degrees of freedom (GDOF) perspective. An inner bound on the GDOF is derived using a combination of techniques such as treating interference as noise, zero forcing (ZF) at the receivers, interference alignment (IA), and extending the Han-Kobayashi (HK) scheme to K users, as a function of the number of antennas and the log INR/log SNR level. Several interesting conclusions are drawn from the derived bounds. It is shown that when K > N/M + 1, a combination of the HK and IA schemes performs the best among the schemes considered. When N/M < K <= N/M + 1, the HK-scheme outperforms other schemes and is found to be GDOF optimal in many cases. In addition, when the SNR and INR are at the same level, ZF-receiving and the HK-scheme have the same GDOF performance.

Minimization Problems Based on Relative alpha-Entropy II: Reverse Projection

In part I of this two-part work, certain minimization problems based on a parametric family of relative entropies (denoted I-alpha) were studied. Such minimizers were called forward I-alpha-projections. Here, a complementary class of minimization problems leading to the so-called reverse I-alpha-projections are studied. Reverse I-alpha-projections, particularly on log-convex or power-law families, are of interest in robust estimation problems (alpha > 1) and in constrained compression settings (alpha < 1). Orthogonality of the power-law family with an associated linear family is first established and is then exploited to turn a reverse I-alpha-projection into a forward I-alpha-projection. The transformed problem is a simpler quasi-convex minimization subject to linear constraints.

Influence of Anisotropy on Pavement Responses Using Adaptive Sparse Polynomial Chaos Expansion

Modeling the spatial variability that exists in pavement systems can be conveniently represented by means of random fields; in this study, a probabilistic analysis that considers the spatial variability, including the anisotropic nature of the pavement layer properties, is presented. The integration of the spatially varying log-normal random fields into a linear-elastic finite difference analysis has been achieved through the expansion optimal linear estimation method. For the estimation of the critical pavement responses, metamodels based on polynomial chaos expansion (PCE) are developed to replace the computationally expensive finite-difference model. The sparse polynomial chaos expansion based on an adaptive regression-based algorithm, and enhanced by the combined use of the global sensitivity analysis (GSA) is used, with significant savings in computational effort. The effect of anisotropy in each layer on the pavement responses was studied separately, and an effort is made to identify the pavement layer wherein the introduction of anisotropic characteristics results in the most significant impact on the critical strains. It is observed that the anisotropy in the base layer has a significant but diverse effect on both critical strains. While the compressive strain tends to be considerably higher than that observed for the isotropic section, the tensile strains show a decrease in the mean value with the introduction of base-layer anisotropy. Furthermore, asphalt-layer anisotropy also tends to decrease the critical tensile strain while having little effect on the critical compressive strain. (C) 2015 American Society of Civil Engineers.

DETERMINISTIC ALGORITHMS FOR MATCHING AND PACKING PROBLEMS BASED ON REPRESENTATIVE SETS

In this work, we study the well-known r-DIMENSIONAL k-MATCHING ((r, k)-DM), and r-SET k-PACKING ((r, k)-SP) problems. Given a universe U := U-1 ... U-r and an r-uniform family F subset of U-1 x ... x U-r, the (r, k)-DM problem asks if F admits a collection of k mutually disjoint sets. Given a universe U and an r-uniform family F subset of 2(U), the (r, k)-SP problem asks if F admits a collection of k mutually disjoint sets. We employ techniques based on dynamic programming and representative families. This leads to a deterministic algorithm with running time O(2.851((r-1)k) .vertical bar F vertical bar. n log(2)n . logW) for the weighted version of (r, k)-DM, where W is the maximum weight in the input, and a deterministic algorithm with running time O(2.851((r-0.5501)k).vertical bar F vertical bar.n log(2) n . logW) for the weighted version of (r, k)-SP. Thus, we significantly improve the previous best known deterministic running times for (r, k)-DM and (r, k)-SP and the previous best known running times for their weighted versions. We rely on structural properties of (r, k)-DM and (r, k)-SP to develop algorithms that are faster than those that can be obtained by a standard use of representative sets. Incorporating the principles of iterative expansion, we obtain a better algorithm for (3, k)-DM, running in time O(2.004(3k).vertical bar F vertical bar . n log(2)n). We believe that this algorithm demonstrates an interesting application of representative families in conjunction with more traditional techniques. Furthermore, we present kernels of size O(e(r)r(k-1)(r) logW) for the weighted versions of (r, k)-DM and (r, k)-SP, improving the previous best known kernels of size O(r!r(k-1)(r) logW) for these problems.

Upper bound on cubicity in terms of boxicity for graphs of low chromatic number

The boxicity (respectively cubicity) of a graph G is the least integer k such that G can be represented as an intersection graph of axis-parallel k-dimensional boxes (respectively k-dimensional unit cubes) and is denoted by box(G) (respectively cub(G)). It was shown by Adiga and Chandran (2010) that for any graph G, cub(G) <= box(G) log(2) alpha(G], where alpha(G) is the maximum size of an independent set in G. In this note we show that cub(G) <= 2 log(2) X (G)] box(G) + X (G) log(2) alpha(G)], where x (G) is the chromatic number of G. This result can provide a much better upper bound than that of Adiga and Chandran for graph classes with bounded chromatic number. For example, for bipartite graphs we obtain cub(G) <= 2(box(G) + log(2) alpha(G)] Moreover, we show that for every positive integer k, there exist graphs with chromatic number k such that for every epsilon > 0, the value given by our upper bound is at most (1 + epsilon) times their cubicity. Thus, our upper bound is almost tight. (c) 2015 Elsevier B.V. All rights reserved.